{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#   面部检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"faceId\": \"9c933e92-1a33-4c43-8e57-2a32ad308953\", \"faceRectangle\": {\"top\": 66, \"left\": 92, \"width\": 66, \"height\": 66}, \"faceAttributes\": {\"smile\": 0.0, \"headPose\": {\"pitch\": 8.2, \"roll\": -0.8, \"yaw\": 0.4}, \"gender\": \"male\", \"age\": 21.0, \"facialHair\": {\"moustache\": 0.1, \"beard\": 0.1, \"sideburns\": 0.1}, \"glasses\": \"NoGlasses\", \"emotion\": {\"anger\": 0.0, \"contempt\": 0.0, \"disgust\": 0.0, \"fear\": 0.0, \"happiness\": 0.0, \"neutral\": 0.999, \"sadness\": 0.0, \"surprise\": 0.0}, \"blur\": {\"blurLevel\": \"low\", \"value\": 0.17}, \"exposure\": {\"exposureLevel\": \"goodExposure\", \"value\": 0.47}, \"noise\": {\"noiseLevel\": \"low\", \"value\": 0.0}, \"makeup\": {\"eyeMakeup\": true, \"lipMakeup\": true}, \"accessories\": [], \"occlusion\": {\"foreheadOccluded\": false, \"eyeOccluded\": false, \"mouthOccluded\": false}, \"hair\": {\"bald\": 0.03, \"invisible\": false, \"hairColor\": [{\"color\": \"black\", \"confidence\": 1.0}, {\"color\": \"other\", \"confidence\": 0.75}, {\"color\": \"gray\", \"confidence\": 0.66}, {\"color\": \"brown\", \"confidence\": 0.17}, {\"color\": \"blond\", \"confidence\": 0.04}, {\"color\": \"red\", \"confidence\": 0.01}, {\"color\": \"white\", \"confidence\": 0.0}]}}}]'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-1 面部检测\n",
    "import requests\n",
    "import json\n",
    "\n",
    "# set to your own subscription key value\n",
    "subscription_key = \"f989fceb9e0248129206f8072802ed1b\"\n",
    "assert subscription_key\n",
    "\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-hzh.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文body\n",
    "image_url = 'http://li_yangrui.gitee.io/picture-storage/OIP.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数parameters\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->bytes\n",
    "json.dumps(response.json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## json转义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': '9c933e92-1a33-4c43-8e57-2a32ad308953',\n",
       "  'faceRectangle': {'top': 66, 'left': 92, 'width': 66, 'height': 66},\n",
       "  'faceAttributes': {'smile': 0.0,\n",
       "   'headPose': {'pitch': 8.2, 'roll': -0.8, 'yaw': 0.4},\n",
       "   'gender': 'male',\n",
       "   'age': 21.0,\n",
       "   'facialHair': {'moustache': 0.1, 'beard': 0.1, 'sideburns': 0.1},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.0,\n",
       "    'neutral': 0.999,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.17},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.47},\n",
       "   'noise': {'noiseLevel': 'low', 'value': 0.0},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.03,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'other', 'confidence': 0.75},\n",
       "     {'color': 'gray', 'confidence': 0.66},\n",
       "     {'color': 'brown', 'confidence': 0.17},\n",
       "     {'color': 'blond', 'confidence': 0.04},\n",
       "     {'color': 'red', 'confidence': 0.01},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-2\n",
    "results = response.json()\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas 数据表格化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceRectangle.top</th>\n",
       "      <th>faceRectangle.left</th>\n",
       "      <th>faceRectangle.width</th>\n",
       "      <th>faceRectangle.height</th>\n",
       "      <th>faceAttributes.smile</th>\n",
       "      <th>faceAttributes.headPose.pitch</th>\n",
       "      <th>faceAttributes.headPose.roll</th>\n",
       "      <th>faceAttributes.headPose.yaw</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "      <th>...</th>\n",
       "      <th>faceAttributes.noise.value</th>\n",
       "      <th>faceAttributes.makeup.eyeMakeup</th>\n",
       "      <th>faceAttributes.makeup.lipMakeup</th>\n",
       "      <th>faceAttributes.accessories</th>\n",
       "      <th>faceAttributes.occlusion.foreheadOccluded</th>\n",
       "      <th>faceAttributes.occlusion.eyeOccluded</th>\n",
       "      <th>faceAttributes.occlusion.mouthOccluded</th>\n",
       "      <th>faceAttributes.hair.bald</th>\n",
       "      <th>faceAttributes.hair.invisible</th>\n",
       "      <th>faceAttributes.hair.hairColor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9c933e92-1a33-4c43-8e57-2a32ad308953</td>\n",
       "      <td>66</td>\n",
       "      <td>92</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>male</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.03</td>\n",
       "      <td>False</td>\n",
       "      <td>[{'color': 'black', 'confidence': 1.0}, {'colo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId  faceRectangle.top  \\\n",
       "0  9c933e92-1a33-4c43-8e57-2a32ad308953                 66   \n",
       "\n",
       "   faceRectangle.left  faceRectangle.width  faceRectangle.height  \\\n",
       "0                  92                   66                    66   \n",
       "\n",
       "   faceAttributes.smile  faceAttributes.headPose.pitch  \\\n",
       "0                   0.0                            8.2   \n",
       "\n",
       "   faceAttributes.headPose.roll  faceAttributes.headPose.yaw  \\\n",
       "0                          -0.8                          0.4   \n",
       "\n",
       "  faceAttributes.gender  ...  faceAttributes.noise.value  \\\n",
       "0                  male  ...                         0.0   \n",
       "\n",
       "   faceAttributes.makeup.eyeMakeup  faceAttributes.makeup.lipMakeup  \\\n",
       "0                             True                             True   \n",
       "\n",
       "   faceAttributes.accessories faceAttributes.occlusion.foreheadOccluded  \\\n",
       "0                          []                                     False   \n",
       "\n",
       "   faceAttributes.occlusion.eyeOccluded  \\\n",
       "0                                 False   \n",
       "\n",
       "   faceAttributes.occlusion.mouthOccluded  faceAttributes.hair.bald  \\\n",
       "0                                   False                      0.03   \n",
       "\n",
       "   faceAttributes.hair.invisible  \\\n",
       "0                          False   \n",
       "\n",
       "                       faceAttributes.hair.hairColor  \n",
       "0  [{'color': 'black', 'confidence': 1.0}, {'colo...  \n",
       "\n",
       "[1 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A-3\n",
    "import pandas as pd\n",
    "df_face = pd.json_normalize(results)\n",
    "df_face"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9c933e92-1a33-4c43-8e57-2a32ad308953']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faceID = df_face['faceId'].values.tolist()\n",
    "faceID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['faceId', 'faceRectangle.top', 'faceRectangle.left',\n",
       "       'faceRectangle.width', 'faceRectangle.height', 'faceAttributes.smile',\n",
       "       'faceAttributes.headPose.pitch', 'faceAttributes.headPose.roll',\n",
       "       'faceAttributes.headPose.yaw', 'faceAttributes.gender',\n",
       "       'faceAttributes.age', 'faceAttributes.facialHair.moustache',\n",
       "       'faceAttributes.facialHair.beard',\n",
       "       'faceAttributes.facialHair.sideburns', 'faceAttributes.glasses',\n",
       "       'faceAttributes.emotion.anger', 'faceAttributes.emotion.contempt',\n",
       "       'faceAttributes.emotion.disgust', 'faceAttributes.emotion.fear',\n",
       "       'faceAttributes.emotion.happiness', 'faceAttributes.emotion.neutral',\n",
       "       'faceAttributes.emotion.sadness', 'faceAttributes.emotion.surprise',\n",
       "       'faceAttributes.blur.blurLevel', 'faceAttributes.blur.value',\n",
       "       'faceAttributes.exposure.exposureLevel',\n",
       "       'faceAttributes.exposure.value', 'faceAttributes.noise.noiseLevel',\n",
       "       'faceAttributes.noise.value', 'faceAttributes.makeup.eyeMakeup',\n",
       "       'faceAttributes.makeup.lipMakeup', 'faceAttributes.accessories',\n",
       "       'faceAttributes.occlusion.foreheadOccluded',\n",
       "       'faceAttributes.occlusion.eyeOccluded',\n",
       "       'faceAttributes.occlusion.mouthOccluded', 'faceAttributes.hair.bald',\n",
       "       'faceAttributes.hair.invisible', 'faceAttributes.hair.hairColor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查属性/特征值\n",
    "df_face.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>faceId</th>\n",
       "      <th>faceAttributes.glasses</th>\n",
       "      <th>faceAttributes.emotion.neutral</th>\n",
       "      <th>faceAttributes.age</th>\n",
       "      <th>faceAttributes.gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9c933e92-1a33-4c43-8e57-2a32ad308953</td>\n",
       "      <td>NoGlasses</td>\n",
       "      <td>0.999</td>\n",
       "      <td>21.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 faceId faceAttributes.glasses  \\\n",
       "0  9c933e92-1a33-4c43-8e57-2a32ad308953              NoGlasses   \n",
       "\n",
       "   faceAttributes.emotion.neutral  faceAttributes.age faceAttributes.gender  \n",
       "0                           0.999                21.0                  male  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可观察其中几组数据\n",
    "df_face[['faceId','faceAttributes.glasses','faceAttributes.emotion.neutral','faceAttributes.age','faceAttributes.gender']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸相似度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## create facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "# 1、create  list列表\n",
    "# faceListId\n",
    "faceListId = \"lee\" # 学生填写设置人脸列表ID\n",
    "create_facelists_url = 'https://api-hzh.cognitiveservices.azure.com/face/v1.0/facelists/{}' # 学生填写 ☆ 注意此条url修改\n",
    "subscription_key = \"f989fceb9e0248129206f8072802ed1b\"\n",
    "assert subscription_key\n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key ,\n",
    "}\n",
    "data = {\n",
    "    \"name\": \"lee\",\n",
    "    \"userData\": \"图库\",\n",
    "    \"recognitionModel\": \"recognition_03\",\n",
    "    \n",
    "}\n",
    "\n",
    "r_create = requests.put(create_facelists_url.format(faceListId),headers=headers,json=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b''"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_create.content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get facelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url =\"https://api-hzh.cognitiveservices.azure.com/face/v1.0/facelists/{}\" # 学生填写\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [], 'faceListId': 'lee', 'name': 'lee', 'userData': '图库'}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_get_facelist.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 添加人脸"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#先加一张脸试试\n",
    "# 2、Add face\n",
    "add_face_url =\"https://api-hzh.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedfaces\"\n",
    "\n",
    "assert subscription_key\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "img_url = \"http://li_yangrui.gitee.io/picture-storage/OIP.jpg\"\n",
    "\n",
    "params_add_face={\n",
    "    \"userData\":\"汪苏泷\"\n",
    "}\n",
    "\n",
    "r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_add_face.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaceId': 'f027cd04-39a5-430e-96c2-c7e3d90052ee'}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_add_face.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 封装成函数方便多次使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 封装成函数方便添加图片/函数——可以重复使用相同的功能\n",
    "def AddFace(img_url=str,userData=str):\n",
    "    add_face_url =\"https://api-hzh.cognitiveservices.azure.com/face/v1.0/facelists/{}/persistedFaces\"\n",
    "    assert subscription_key\n",
    "    headers = {\n",
    "        # Request headers\n",
    "        'Content-Type': 'application/json',\n",
    "        'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "    }\n",
    "    img_url = img_url\n",
    "\n",
    "    params_add_face={\n",
    "        \"userData\":userData\n",
    "    }\n",
    "    r_add_face = requests.post(add_face_url.format(faceListId),headers=headers,params=params_add_face,json={\"url\":img_url})\n",
    "    return r_add_face.status_code#返回出状态码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AddFace(\"http://li_yangrui.gitee.io/picture-storage/OIP.jpg\",\"汪苏泷\")\n",
    "AddFace(\"http://li_yangrui.gitee.io/picture-storage/li3.jpg\",\"黎杨锐\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'persistedFaces': [{'persistedFaceId': 'f027cd04-39a5-430e-96c2-c7e3d90052ee',\n",
       "   'userData': '汪苏泷'},\n",
       "  {'persistedFaceId': '7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258',\n",
       "   'userData': '汪苏泷'},\n",
       "  {'persistedFaceId': '772db461-dda4-4b7a-a96f-b99e7cfc601d',\n",
       "   'userData': '黎杨锐'}],\n",
       " 'faceListId': 'lee',\n",
       " 'name': 'lee',\n",
       " 'userData': '图库'}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查你的facelist的信息\n",
    "get_facelist_url = \"https://api-hzh.cognitiveservices.azure.com/face/v1.0/facelists/{}\"\n",
    "r_get_facelist = requests.get(get_facelist_url.format(faceListId),headers=headers,params=data)\n",
    "r_get_facelist.json()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find similars 返回相似置信度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'faceId': 'f817a2b6-36cf-43aa-82de-820c489c9696',\n",
       "  'faceRectangle': {'top': 66, 'left': 92, 'width': 66, 'height': 66},\n",
       "  'faceAttributes': {'smile': 0.0,\n",
       "   'headPose': {'pitch': 8.2, 'roll': -0.8, 'yaw': 0.4},\n",
       "   'gender': 'male',\n",
       "   'age': 21.0,\n",
       "   'facialHair': {'moustache': 0.1, 'beard': 0.1, 'sideburns': 0.1},\n",
       "   'glasses': 'NoGlasses',\n",
       "   'emotion': {'anger': 0.0,\n",
       "    'contempt': 0.0,\n",
       "    'disgust': 0.0,\n",
       "    'fear': 0.0,\n",
       "    'happiness': 0.0,\n",
       "    'neutral': 0.999,\n",
       "    'sadness': 0.0,\n",
       "    'surprise': 0.0},\n",
       "   'blur': {'blurLevel': 'low', 'value': 0.17},\n",
       "   'exposure': {'exposureLevel': 'goodExposure', 'value': 0.47},\n",
       "   'makeup': {'eyeMakeup': True, 'lipMakeup': True},\n",
       "   'accessories': [],\n",
       "   'occlusion': {'foreheadOccluded': False,\n",
       "    'eyeOccluded': False,\n",
       "    'mouthOccluded': False},\n",
       "   'hair': {'bald': 0.03,\n",
       "    'invisible': False,\n",
       "    'hairColor': [{'color': 'black', 'confidence': 1.0},\n",
       "     {'color': 'other', 'confidence': 0.75},\n",
       "     {'color': 'gray', 'confidence': 0.66},\n",
       "     {'color': 'brown', 'confidence': 0.17},\n",
       "     {'color': 'blond', 'confidence': 0.04},\n",
       "     {'color': 'red', 'confidence': 0.01},\n",
       "     {'color': 'white', 'confidence': 0.0}]}}}]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect 检测人脸的id\n",
    "# replace <My Endpoint String> with the string from your endpoint URL\n",
    "face_api_url = 'https://api-hzh.cognitiveservices.azure.com/face/v1.0/detect'\n",
    "\n",
    "# 请求正文\n",
    "image_url = 'http://li_yangrui.gitee.io/picture-storage/OIP.jpg'\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "\n",
    "# 请求参数\n",
    "params = {\n",
    "    'returnFaceId': 'true',\n",
    "    'returnFaceLandmarks': 'false',\n",
    "    # 选择model\n",
    "    'recognitionModel':'recognition_03',#此参数需与facelist参数一致\n",
    "    'detectionModel':'detection_01',\n",
    "    # 可选参数,请仔细阅读API文档\n",
    "    'returnFaceAttributes': 'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure',\n",
    "}\n",
    "\n",
    "response = requests.post(face_api_url, params=params,\n",
    "                         headers=headers, json={\"url\": image_url})\n",
    "# json.dumps 将json--->字符串\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "findsimilars_url = \"https://api-hzh.cognitiveservices.azure.com/face/v1.0/findsimilars\"\n",
    "\n",
    "# 请求正文 faceId需要先检测一张照片获取\n",
    "data_findsimilars = {\n",
    "    \"faceId\":\"f817a2b6-36cf-43aa-82de-820c489c9696\",#取上方的faceID\n",
    "    \"faceListId\": \"lee\",\n",
    "#     \"faceIds\":faceId_02,\n",
    "    \"maxNumOfCandidatesReturned\": 3,\n",
    "    \"mode\": \"matchFace\"#matchPerson #一种为验证模式，一种为相似值模式\n",
    "    }\n",
    "\n",
    "r_findsimilars = requests.post(findsimilars_url,headers=headers,json=data_findsimilars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'persistedFaceId': 'f027cd04-39a5-430e-96c2-c7e3d90052ee',\n",
       "  'confidence': 1.0},\n",
       " {'persistedFaceId': '7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258',\n",
       "  'confidence': 1.0},\n",
       " {'persistedFaceId': '772db461-dda4-4b7a-a96f-b99e7cfc601d',\n",
       "  'confidence': 0.09263}]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_findsimilars.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f027cd04-39a5-430e-96c2-c7e3d90052ee</td>\n",
       "      <td>汪苏泷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258</td>\n",
       "      <td>汪苏泷</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>772db461-dda4-4b7a-a96f-b99e7cfc601d</td>\n",
       "      <td>黎杨锐</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId userData\n",
       "0  f027cd04-39a5-430e-96c2-c7e3d90052ee      汪苏泷\n",
       "1  7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258      汪苏泷\n",
       "2  772db461-dda4-4b7a-a96f-b99e7cfc601d      黎杨锐"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "adf = pd.json_normalize(r_get_facelist.json()[\"persistedFaces\"])# 升级pandas才能运行\n",
    "adf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f027cd04-39a5-430e-96c2-c7e3d90052ee</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>772db461-dda4-4b7a-a96f-b99e7cfc601d</td>\n",
       "      <td>0.09263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId  confidence\n",
       "0  f027cd04-39a5-430e-96c2-c7e3d90052ee     1.00000\n",
       "1  7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258     1.00000\n",
       "2  772db461-dda4-4b7a-a96f-b99e7cfc601d     0.09263"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdf = pd.json_normalize(r_findsimilars.json())# 升级pandas才能运行\n",
    "bdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>persistedFaceId</th>\n",
       "      <th>userData</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f027cd04-39a5-430e-96c2-c7e3d90052ee</td>\n",
       "      <td>汪苏泷</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258</td>\n",
       "      <td>汪苏泷</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>772db461-dda4-4b7a-a96f-b99e7cfc601d</td>\n",
       "      <td>黎杨锐</td>\n",
       "      <td>0.09263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        persistedFaceId userData  confidence\n",
       "0  f027cd04-39a5-430e-96c2-c7e3d90052ee      汪苏泷     1.00000\n",
       "1  7f1a0d4a-2c7a-4bd2-88ff-8bd0de618258      汪苏泷     1.00000\n",
       "2  772db461-dda4-4b7a-a96f-b99e7cfc601d      黎杨锐     0.09263"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(adf, bdf,how='inner', on='persistedFaceId').sort_values(by=\"confidence\",ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect face 删除列表内人脸id\n",
    "faceListId = \"lee\"\n",
    "delete_face_url =\"https://api-hzh.cognitiveservices.azure.com/face/v1.0/facelists/lee/persistedfaces/88f0e641-1de8-4a2e-97d2-c17557afeaae\"\n",
    "\n",
    "assert subscription_key\n",
    "# 例如：删除黄志毅： {'persistedFaceId': '69103b48-b6c4-4f58-8ac1-4c8b84e56bc1','userData': '黄智毅'},\n",
    "\n",
    "\n",
    "persistedFaceId = \"f027cd04-39a5-430e-96c2-c7e3d90052ee\"\n",
    "# 直接取上面获得的ID{'persistedFaceId': 'f18450d3-60d2-45f3-a69e-783574dc3ce8'} \n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "\n",
    "# 注意requests请求为delete\n",
    "r_delete_face = requests.delete(delete_face_url,headers=headers,params=persistedFaceId)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [404]>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_delete_face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect face 删除列表内人脸列表\n",
    "import requests\n",
    "delete_face_url =\"https://api-hzh.cognitiveservices.azure.com/face/v1.0/facelists/lee\"#学生填写\n",
    "assert subscription_key\n",
    "\n",
    "headers = {\n",
    "    # Request headers\n",
    "    'Content-Type': 'application/json',\n",
    "    'Ocp-Apim-Subscription-Key': subscription_key,\n",
    "}\n",
    "\n",
    "# format内直接删除\n",
    "# 注意requests请求为delete\n",
    "r_delete_facelist = requests.delete(delete_face_url,headers=headers)#学生填写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_delete_facelist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Face++ FaceSets 实践"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_secret = \"rUH-6-qF65R31DqjQLxnsJmMBIGDvZ16\"\n",
    "api_key = \"ICRMxo2r9I2kGj2UaciZhdywVRizPcF8\"  # Replace with a valid Subscription Key here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Create（创建人脸集合）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. FaceSet Create\n",
    "import requests,json\n",
    "\n",
    "display_name = \"黎杨锐\"\n",
    "outer_id = \"00001\"\n",
    "user_data = \"2人，2男生\"\n",
    "\n",
    "CreateFace_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/create\"\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'display_name':display_name,\n",
    "    'outer_id':outer_id,\n",
    "    'user_data':user_data\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(CreateFace_Url, params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 66,\n",
       " 'error_message': 'FACESET_EXIST',\n",
       " 'request_id': '1603531736,ad646d6d-6f9e-4598-ba99-824726a563a5'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet GetDetail（获取人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "GetDetail_Url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/getdetail\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'outer_id':outer_id,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(GetDetail_Url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '48edd7a196b52219e9005166aec4b43b',\n",
       " 'tags': '',\n",
       " 'time_used': 67,\n",
       " 'user_data': '2人，2男生',\n",
       " 'display_name': '黎杨锐',\n",
       " 'face_tokens': [],\n",
       " 'face_count': 0,\n",
       " 'request_id': '1603531219,0ed1f9bb-433e-48bd-9a67-ddb057cc83f0',\n",
       " 'outer_id': '00001'}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet AddFace（增加人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "AddFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/addface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'48edd7a196b52219e9005166aec4b43b',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(AddFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '48edd7a196b52219e9005166aec4b43b',\n",
       " 'time_used': 65,\n",
       " 'face_count': 0,\n",
       " 'face_added': 0,\n",
       " 'request_id': '1603531223,08e98385-53db-4253-b07f-1bd070cfa348',\n",
       " 'outer_id': '00001',\n",
       " 'failure_detail': [{'reason': 'INVALID_FACE_TOKEN',\n",
       "   'face_token': 'b0407b9e803ebd39d511cd7956fd5bf5'}]}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet RemoveFace（移除人脸信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "RemoveFace_url = \" https://api-cn.faceplusplus.com/facepp/v3/faceset/removeface\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'48edd7a196b52219e9005166aec4b43b',\n",
    "    'face_tokens':'b0407b9e803ebd39d511cd7956fd5bf5',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(RemoveFace_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '48edd7a196b52219e9005166aec4b43b',\n",
       " 'face_removed': 0,\n",
       " 'time_used': 123,\n",
       " 'face_count': 0,\n",
       " 'request_id': '1603531224,36e4c4d4-df5e-4b60-ab88-5c51217c6e1c',\n",
       " 'outer_id': '00001',\n",
       " 'failure_detail': [{'reason': 'FACE_NOT_IN_FACESET',\n",
       "   'face_token': 'b0407b9e803ebd39d511cd7956fd5bf5'}]}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FaceSet Update（更新人脸集合信息）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "Update_url = \"https://api-cn.faceplusplus.com/facepp/v3/faceset/update\"\n",
    "\n",
    "payload = {\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'faceset_token':'48edd7a196b52219e9005166aec4b43b',\n",
    "    'user_data':\"2人，2男生\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Update_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faceset_token': '48edd7a196b52219e9005166aec4b43b',\n",
       " 'request_id': '1603531226,d409128d-d76f-4611-aeb4-475897150523',\n",
       " 'time_used': 73,\n",
       " 'outer_id': '00001'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Face（对比人脸相似度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "wangsulong = \"http://li_yangrui.gitee.io/picture-storage/OIP.jpg\"\n",
    "liyangrui = \"http://li_yangrui.gitee.io/picture-storage/li3.jpg\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案1:直接对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "Compare_url = \"https://api-cn.faceplusplus.com/facepp/v3/compare\"\n",
    "\n",
    "payload ={\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'image_url1':wangsulong,\n",
    "    'image_url2':liyangrui\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Compare_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'faces1': [{'face_rectangle': {'width': 67,\n",
       "    'top': 72,\n",
       "    'left': 90,\n",
       "    'height': 67},\n",
       "   'face_token': 'b7c430ba15de15e42f72c98dc530cad2'}],\n",
       " 'faces2': [{'face_rectangle': {'width': 245,\n",
       "    'top': 218,\n",
       "    'left': 100,\n",
       "    'height': 245},\n",
       "   'face_token': '29efae8cdf3db4d96a39fc0a18cf60ad'}],\n",
       " 'time_used': 1046,\n",
       " 'thresholds': {'1e-3': 62.327, '1e-5': 73.975, '1e-4': 69.101},\n",
       " 'confidence': 55.67,\n",
       " 'image_id2': 'da5XCjj6kB7JpcXtjA06ZQ==',\n",
       " 'image_id1': 'ryW3r6NskyeVZB/vSFRurQ==',\n",
       " 'request_id': '1603529809,04438423-3dd7-494d-98ae-2aa6ad0a73fe'}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方案2:与人脸集合进行对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 面部检测(获取face_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "Detect_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect' \n",
    "img_url = wangsulong\n",
    "\n",
    "payload = {\n",
    "    \"image_url\":img_url,\n",
    "    'api_key': api_key,\n",
    "    'api_secret': api_secret,\n",
    "    'return_attributes':'gender,age,smiling,emotion', \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.post(Detect_url,params=payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1603529879,37984b09-a5a7-43bc-8ff4-b6d82b79ae8f',\n",
       " 'time_used': 821,\n",
       " 'faces': [{'face_token': '7a775bc6f88e20ba46c28438fb4a3f4e',\n",
       "   'face_rectangle': {'top': 72, 'left': 90, 'width': 67, 'height': 67},\n",
       "   'attributes': {'gender': {'value': 'Female'},\n",
       "    'age': {'value': 22},\n",
       "    'smile': {'value': 0.62, 'threshold': 50.0},\n",
       "    'emotion': {'anger': 0.001,\n",
       "     'disgust': 0.469,\n",
       "     'fear': 0.004,\n",
       "     'happiness': 0.001,\n",
       "     'neutral': 0.64,\n",
       "     'sadness': 0.001,\n",
       "     'surprise': 98.883}}}],\n",
       " 'image_id': 'ryW3r6NskyeVZB/vSFRurQ==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 讯飞人脸对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"code\":\"0\",\"data\":0.384127,\"desc\":\"success\",\"sid\":\"wsr00038e91@dx133a12ef3b6d6f2300\"}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import time\n",
    "import json\n",
    "import hashlib\n",
    "import base64\n",
    "\n",
    "\n",
    "def main():\n",
    "    # 应用ID (必须为webapi类型应用，并开通人脸比对服务，参考帖子如何创建一个webapi应用：http://bbs.xfyun.cn/forum.php?mod=viewthread&tid=36481)\n",
    "    x_appid = '5f92dfff'\n",
    "    # 接口密钥(webapi类型应用开通人脸比对服务后，控制台--我的应用---人脸比对---相应服务的apikey)\n",
    "    api_key = '18d229b429f538a51030bfb2c4d3a63c'\n",
    "    # webapi接口地址\n",
    "    url = 'http://api.xfyun.cn/v1/service/v1/image_identify/face_verification'\n",
    "    # 组装http请求头\n",
    "    x_time = str(int(time.time()))\n",
    "    param = {'auto_rotate': True}\n",
    "    param = json.dumps(param)\n",
    "    x_param = base64.b64encode(param.encode('utf-8'))\n",
    "    m2 = hashlib.md5()\n",
    "    m2.update(str(api_key + x_time + str(x_param, 'utf-8')).encode('utf-8'))\n",
    "    x_checksum = m2.hexdigest()\n",
    "    x_header = {\n",
    "        'X-Appid': x_appid,\n",
    "        'X-CurTime': x_time,\n",
    "        'X-CheckSum': x_checksum,\n",
    "        'X-Param': x_param,\n",
    "    }\n",
    "    # 对图片一和图片二base64编码\n",
    "    with open(r'C:\\相册\\汪苏泷.jpg', 'rb') as f:\n",
    "        f1 = f.read()\n",
    "    with open(r'C:\\相册\\黎杨锐.jpg', 'rb') as f:\n",
    "        f2 = f.read()\n",
    "    f1_base64 = str(base64.b64encode(f1), 'utf-8')\n",
    "    f2_base64 = str(base64.b64encode(f2), 'utf-8')\n",
    "    data = {\n",
    "        'first_image': f1_base64,\n",
    "        'second_image': f2_base64,\n",
    "    }\n",
    "    req = requests.post(url, data=data, headers=x_header)\n",
    "    result = str(req.content, 'utf-8')\n",
    "    print(result)  # 错误码链接：https://www.xfyun.cn/document/error-code (code返回错误码时必看)\n",
    "    return\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 计算机视觉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析远程图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"categories\": [{\"name\": \"people_portrait\", \"score\": 0.78515625, \"detail\": {\"celebrities\": []}}], \"color\": {\"dominantColorForeground\": \"White\", \"dominantColorBackground\": \"White\", \"dominantColors\": [\"White\"], \"accentColor\": \"4F5E71\", \"isBwImg\": false, \"isBWImg\": false}, \"description\": {\"tags\": [\"person\", \"necktie\", \"indoor\", \"young\", \"boy\", \"posing\", \"clothing\", \"photo\", \"man\", \"wearing\", \"camera\", \"shirt\", \"holding\", \"smiling\", \"front\", \"looking\", \"hair\", \"standing\", \"woman\", \"little\", \"dressed\", \"neck\", \"suit\", \"blue\"], \"captions\": [{\"text\": \"a person posing for the camera\", \"confidence\": 0.9730742070246836}]}, \"requestId\": \"7d9d44f4-04e8-44f7-8385-1865ad73ded1\", \"metadata\": {\"height\": 539, \"width\": 427, \"format\": \"Jpeg\"}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "endpoint = \"https://api-jisjsj.cognitiveservices.azure.com/\"\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "subscription_key = \"66b6acbb7ec94332a183318c109f4a87\"\n",
    "\n",
    "# base url\n",
    "analyze_url = endpoint+ \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://li_yangrui.gitee.io/picture-storage/li3.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "# 参数\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "# 请求主体body\n",
    "data = {'url': image_url}\n",
    "response = requests.post(analyze_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(json.dumps(response.json()))\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析本地图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'categories': [{'name': 'people_', 'score': 0.86328125, 'detail': {'celebrities': []}}], 'color': {'dominantColorForeground': 'Black', 'dominantColorBackground': 'Grey', 'dominantColors': ['Black', 'Grey'], 'accentColor': '141C27', 'isBwImg': False, 'isBWImg': False}, 'description': {'tags': ['person', 'clothing', 'necktie', 'indoor', 'boy', 'man', 'young', 'wearing', 'standing', 'small', 'front', 'posing', 'suit', 'bow', 'shirt', 'dressed', 'sitting', 'holding', 'little', 'room', 'neck', 'purple', 'blue'], 'captions': [{'text': 'a young boy wearing a suit and tie', 'confidence': 0.9202006416665521}]}, 'requestId': '50d50a97-edbc-40ac-b0b3-9804c1ae31ab', 'metadata': {'height': 257, 'width': 257, 'format': 'Jpeg'}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "# analyze_url = endpoint + \"vision/v2.1/analyze\"\n",
    "\n",
    "# Set image_path to the local path of an image that you want to analyze.\n",
    "image_path = \"C:\\相册\\汪苏泷.jpg\"\n",
    "# Read the image into a byte array\n",
    "image_data = open(image_path, \"rb\").read()\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"66b6acbb7ec94332a183318c109f4a87\",\n",
    "           'Content-Type': 'application/octet-stream'}\n",
    "params = {'visualFeatures': 'Categories,Description,Color'}\n",
    "response = requests.post(\n",
    "    analyze_url, headers=headers, params=params, data=image_data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The most\n",
    "# relevant caption for the image is obtained from the 'description' property.\n",
    "analysis = response.json()\n",
    "print(analysis)\n",
    "image_caption = analysis[\"description\"][\"captions\"][0][\"text\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the caption.\n",
    "image = Image.open(BytesIO(image_data))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(image_caption, size=\"x-large\", y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成缩略图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thumbnail is 100-by-100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "# if 'COMPUTER_VISION_SUBSCRIPTION_KEY' in os.environ:\n",
    "#     subscription_key = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\n",
    "# else:\n",
    "#     print(\"\\nSet the COMPUTER_VISION_SUBSCRIPTION_KEY environment variable.\\n**Restart your shell or IDE for changes to take effect.**\")\n",
    "#     sys.exit()\n",
    "\n",
    "# if 'COMPUTER_VISION_ENDPOINT' in os.environ:\n",
    "#     endpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n",
    "\n",
    "thumbnail_url = \"https://api-jisjsj.cognitiveservices.azure.com/\" + \"vision/v2.1/generateThumbnail\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://li_yangrui.gitee.io/picture-storage/li3.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': \"66b6acbb7ec94332a183318c109f4a87\"}\n",
    "params = {'width': '100', 'height': '100', 'smartCropping': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(thumbnail_url, headers=headers,\n",
    "                         params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "thumbnail = Image.open(BytesIO(response.content))\n",
    "\n",
    "# Display the thumbnail.\n",
    "plt.imshow(thumbnail)\n",
    "plt.axis(\"off\")\n",
    "\n",
    "# Verify the thumbnail size.\n",
    "print(\"Thumbnail is {0}-by-{1}\".format(*thumbnail.size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
